Effect Size Calculator: Supports Cohen’s d, Hedges’ g, Eta Squared, Partial Eta Squared, Odds Ratio, and Cramér’s V

Effect size is a measure that shows how strong a relationship is between variables or how large the difference is between groups. Unlike p-values, which only show if a result is statistically significant, effect size tells you how meaningful that result is. This makes it an essential part of research, especially in dissertations and journal papers. To help you calculate effect sizes easily, we’ve created this all-in-one Effect Size Calculator. It supports common metrics used in academic research, including Cohen’s d, Hedges’ g, Eta Squared, Partial Eta Squared, Odds Ratio, and Cramér’s V. Just choose the type of effect size, enter your values, and the calculator will handle the rest — instantly and accurately.

How to Use Our Effect Size Calculator

Using our effect size calculator is simple, even if you’re not a statistics expert. Just follow the steps below to get your effect size quickly and accurately.

1. Step 1: Select the Effect Size Type – Start by choosing the effect size you want to calculate. Use the dropdown menu at the top of the calculator to select from options like Cohen’s d, Hedges’ g, Eta Squared, Partial Eta Squared, Odds Ratio, or Cramér’s V.
2. Step 2: Enter the Required Values – After selecting the effect size, the calculator will automatically show only the input fields you need. For example, if you choose Cohen’s d, you’ll be asked to enter the means and standard deviations of two groups. If you choose Odds Ratio, you’ll enter the values from a 2×2 table. You won’t be overwhelmed with too many fields. The tool hides anything that’s not relevant.
3. Step 3: Click “Calculate.” Once you’ve entered all the required values, simply click the Calculate button. The tool will instantly compute your effect size and display the result below.

Note. If you want to start over or try a different effect size, click the Reset button. This will clear the current inputs but keep your selected effect size visible for convenience.

What Is Effect Size and Why Does It Matter?

Effect size is a way of showing how big or strong a relationship is between two variables or groups. It tells us how much impact one factor has on another. For example, if you’re comparing test scores between two teaching methods, the effect size shows how different the results are, not just whether they are different. It gives more detail than just saying something is “significant.”

While statistical significance tells us if a result is likely due to chance, effect size tells us how important that result is in real life. This is why effect size is required in APA-style papers, dissertations, and peer-reviewed journals. It helps researchers and readers understand the practical value of findings, not just whether they passed a significance test. Reporting effect size adds meaning and strength to your research conclusions.

Effect Sizes You Can Compute with Our All-in-One Calculator

Our All-in-One Effect Size Calculator is designed to simplify your analysis by letting you compute the most commonly used effect sizes in research, all in one place. Whether you’re working with group comparisons, categorical data, or ANOVA designs, this tool helps you quickly calculate the exact effect size you need. It’s ideal for students, researchers, and data analysts preparing dissertations, academic papers, or statistical reports.

Below is a simple explanation of each supported effect size, when to use it, and how it’s calculated.

1. Cohen’s d

Cohen’s d is used when you want to compare the means of two independent groups, such as in an independent-samples t-test. It tells you how large the difference is between the two means, relative to the pooled standard deviation. This effect size is widely used in experimental research to show the magnitude of a treatment or intervention effect.

The Cohen’s d formula is given by:

NOTE. You should use Cohen’s d when your sample sizes are moderate to large and approximately equal.

2. Hedges’ g

Hedges’ g is very similar to Cohen’s d. However, it includes a correction for small sample sizes, which makes it more accurate in studies with fewer participants. Like Cohen’s d, it measures the standardized difference between two means, but it reduces the upward bias that Cohen’s d may have when sample sizes are small.

The Hedges’ g formula is:

Where:

• d is the Cohen’s d value
• N is the total sample size from both groups.

3. Eta Squared (η²)

Eta squared is used in ANOVA (Analysis of Variance) to indicate the proportion of total variance in the dependent variable that is associated with the grouping variable (independent variable). It’s ideal for use in a one-way ANOVA when comparing more than two groups.

The formula for eta squared is based on the F-statistic, and is given by:

Where:

• F is the F-value from the ANOVA
• df1​ is the degrees of freedom for the effect
• df2 is the degrees of freedom for the error terms

4. Partial Eta Squared (ηp²)

Partial eta squared is commonly used in factorial or repeated-measures ANOVA designs. It tells you how much variance in the outcome variable is explained by one specific factor, after accounting for other variables in the model. This makes it especially useful when you’re dealing with multiple effects or interactions.

The formula is identical to that of eta squared when using the F-statistic:

Where:

• F is the F-value from the ANOVA
• df1​ is the degrees of freedom for the effect
• df2 is the degrees of freedom for the error terms

NOTE. Despite the same formula structure, the values differ due to the way partial eta squared isolates the effect of a single factor.

5. Odds Ratio (OR)

Odds Ratio is used when analyzing categorical (binary) outcomes. For example, the odds of an event happening in a treatment group versus a control group. It’s commonly applied in epidemiology, clinical trials, and logistic regression analysis. The odds ratio quantifies how strongly the presence or absence of one factor is associated with another.

The formula is:

Where:

• a represents the number of events in the treatment group
• b represents the number of non-events in the treatment group
• c represents the number of events in the control group
• d represents the number of non-events in the control group

An OR of 1 indicates no association; values above or below 1 suggest a positive or negative association, respectively.

5. Cramér’s V

Cramér’s V is an effect size measure for chi-square tests of independence. It is used when you want to assess the strength of association between two categorical variables arranged in a contingency table (e.g., 2×2, 3×3, etc.). It adjusts the chi-square statistic to account for the size of the table and the sample.

The Cramer’s V formula is given by:

Where:

• χ2 is the chi-square statistic
• N is the total sample size
• k is the smaller of rows−1 or columns−1

Cramér’s V ranges from 0 (no association) to 1 (perfect association). This makes it easy to interpret.

Effect Size Interpretation Guide

Getting an effect size is one thing — but understanding what it means is what really matters. A result can be statistically significant but with little practical value. This is where effect size interpretation comes in. It helps you judge whether the effect is small, moderate, or large, based on commonly accepted benchmarks.

In social science research, even small effects can be meaningful, especially when they influence policy, education, health, or human behavior. However, knowing the general guidelines will help you report and explain your findings more clearly.

Here’s a quick reference guide to help you interpret the effect sizes calculated using our tool:

Key Takeaways

• Effect size helps you understand the magnitude or importance of your research results. It goes beyond stating whether or not the results are statistically significant.
• Our All-in-One Effect Size Calculator makes it easy to compute the most common effect sizes using only the inputs you need.
• The tool supports calculations for Cohen’s d, Hedges’ g, Eta Squared, Partial Eta Squared, Odds Ratio, and Cramér’s V.
• Only the relevant input fields are shown based on your selection. Thus, it’s fast, simple, and beginner-friendly.
• Use the Interpretation Guide to better understand what your calculated value means in real-world terms.
• This tool is perfect for students, researchers, and data analysts preparing academic papers or dissertations.

Frequently Asked Questions

Need Help Interpreting Your Results?

Calculating your effect size is just the beginning. Understanding how to interpret and report those results correctly is crucial, especially when preparing a thesis, dissertation, or publication. However, if you’re unsure how to present your findings, we offer dissertation results writing services to help with data analysis and writing the report for you.

In particular, we provide professional support with:

• Effect size interpretation
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• APA-style write-ups and result sections

Still unsure how to report or interpret your results?

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